请和最弱的搭档一起来

Moshe Mash, Igor Rochlin, David Sarne
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引用次数: 3

摘要

本文考虑了当个体的发现对所有个体都有利时,自利主体进行代价高昂的探索的问题。探索的目的是推理当这些信息是先验未知时,代理可以获得的不同机会的性质和价值。虽然在目标是最大化整体预期收益的情况下已经考虑了这个问题,但本文的重点是在智能体自利的情况下,即每个智能体的目标是最大化其个体预期收益。本文给出了模型的均衡分析,考虑了混合均衡和纯均衡。该分析用于证明智能体使用的平衡合作探索策略及其预期收益的两个非直观属性:(a)当使用混合均衡策略时,智能体可能会因为在其环境中有更多的潜在机会而失败;(b)如果智能体可以有额外的智能体加入他们的探索,他们可能会更喜欢能力较差的智能体加入这个过程。
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Join Me with the Weakest Partner, Please
This paper considers the problem of self-interested agents engaged in costly exploration when individual findings benefit all agents. The purpose of the exploration is to reason about the nature and value of the different opportunities available to the agents whenever such information is a priori unknown. While the problem has been considered for the case where the goal is to maximize the overall expected benefit, the focus of this paper is on settings where the agents are self-interested, i.e, each agent's goal is to maximize its individual expected benefit. The paper presents an equilibrium analysis of the model, considering both mixed and pure equilibria. The analysis is used to demonstrate two somehow non-intuitive properties of the equilibrium cooperative exploration strategies used by the agents and their resulting expected payoffs: (a) when using mixed equilibrium strategies, the agents might lose due to having more potential opportunities available for them in their environment, and (b) if the agents can have additional agents join them in the exploration they might prefer the less competent ones to join the process.
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